Wearable computing applications in eHealth
Non-communicable diseases are the leading cause of death and disability worldwide. Almost two thirds of them are linked to the following risk factors: physical inactivity, unhealthy diets, tobacco use, and harmful use of alcohol. At the same time, everyone is surrounded increasingly with wearables,...
Saved in:
| 主要作者: | |
|---|---|
| 格式: | Online |
| 語言: | 英语 |
| 出版: |
FAU University Press
2025
|
| 主題: | |
| 在線閱讀: | ONIX_20251215T160010_9783961472611_46 |
| 標簽: |
沒有標簽, 成為第一個標記此記錄!
|
| _version_ | 1869519024533012480 |
|---|---|
| author | Leutheuser, Heike |
| author_browse | Leutheuser, Heike |
| author_facet | Leutheuser, Heike |
| author_sort | Leutheuser, Heike |
| collection | Directory of Open Access Books |
| description | Non-communicable diseases are the leading cause of death and disability worldwide. Almost two thirds of them are linked to the following risk factors: physical inactivity, unhealthy diets, tobacco use, and harmful use of alcohol. At the same time, everyone is surrounded increasingly with wearables, like smartphones and smartwatches, making wearable computing an integral part of everyday life. The combination of addressing non-communicable diseases with the help of wearables seems one potential solution. In this thesis, methods and algorithms for three wearable computing applications in eHealth are presented: I) mobile breathing analysis, II) mobile electrocardiogram (ECG) analysis, and III) inertial measurement unit (IMU)-based activity recognition. Respiratory inductance plethysmography (RIP) provides an unobtrusive and mobile method for measuring breathing characteristics, avoiding the measurement with flowmeters (FMs) as with them the natural breathing pattern is altered. The output of RIP devices needs to be adjusted to result in correct ventilatory tidal volumes. State of the art methods for adjusting RIP data can only be applied after the actual measurement as these require the simultaneous data acquisition of RIP and FM. In this thesis, novel adjustment algorithms were created enabling the usage of RIP solely, and by this outside the controlled laboratory environment. Respiratory diseases and infections are amongst the most leading deaths group. In future work, it has to be investigated how RIP devices can effectively be used for patients suffering from these conditions. The reasons for mobile ECG analysis were to provide real-time algorithms for two particular scopes. The first scope dealt with identifying arrhythmic beats using only time instances of successive heartbeats – the RR intervals. In the second scope, three high-accurate single-lead, instantaneous P- and T-wave detection algorithms were compared. These two applications could help in addressing cardiovascular diseases that are the number one cause of deaths worldwide. An effective and easy to handle arrhythmia classification algorithm could send people in risk early to a physician. High-accurate P- and T-wave detection algorithms requiring only a single lead are beneficial for all ECG monitoring fields, and at the same time, enhance the patient’s comfort. IMU-based activity recognition provides an objective method for classifying activities addressing the risk factor physical inactivity. Therefore, a common, publicly available dataset DaLiAc was created to enable the comparison of activity recognition algorithms (http://www.activitynet.org/). Using the benchmark dataset DaLiAc, a hierarchical classification system was created that outperformed six state of the art activity recognition algorithms. Recognizing single activities of daily living might increase the awareness of individuals to increase their physical activity as physical inactivity is one of the four leading risk factors for non-communicable diseases. In this thesis, three different wearable computing applications addressing different non-communicable diseases were presented. Wearables have been continuously used for health monitoring in recent years – with still increasing trend – as they bring certain benefits to the user in daily life. This will be enlarged in the future and fostered by ongoing inventions, accumulating knowledge, technological process, and progressive digitalization. |
| format | Online |
| id | doab-20.500.12854ir-170242 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | FAU University Press |
| publisherStr | FAU University Press |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1702422025-12-16T05:34:09Z Wearable computing applications in eHealth Leutheuser, Heike Wearable Computing Smart Device Funktionsdiagnostik Wearable Computer Gesundheitstelematik thema EDItEUR::U Computing and Information Technology::UY Computer science::UYZ Human–computer interaction thema EDItEUR::U Computing and Information Technology::UK Computer hardware thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning Non-communicable diseases are the leading cause of death and disability worldwide. Almost two thirds of them are linked to the following risk factors: physical inactivity, unhealthy diets, tobacco use, and harmful use of alcohol. At the same time, everyone is surrounded increasingly with wearables, like smartphones and smartwatches, making wearable computing an integral part of everyday life. The combination of addressing non-communicable diseases with the help of wearables seems one potential solution. In this thesis, methods and algorithms for three wearable computing applications in eHealth are presented: I) mobile breathing analysis, II) mobile electrocardiogram (ECG) analysis, and III) inertial measurement unit (IMU)-based activity recognition. Respiratory inductance plethysmography (RIP) provides an unobtrusive and mobile method for measuring breathing characteristics, avoiding the measurement with flowmeters (FMs) as with them the natural breathing pattern is altered. The output of RIP devices needs to be adjusted to result in correct ventilatory tidal volumes. State of the art methods for adjusting RIP data can only be applied after the actual measurement as these require the simultaneous data acquisition of RIP and FM. In this thesis, novel adjustment algorithms were created enabling the usage of RIP solely, and by this outside the controlled laboratory environment. Respiratory diseases and infections are amongst the most leading deaths group. In future work, it has to be investigated how RIP devices can effectively be used for patients suffering from these conditions. The reasons for mobile ECG analysis were to provide real-time algorithms for two particular scopes. The first scope dealt with identifying arrhythmic beats using only time instances of successive heartbeats – the RR intervals. In the second scope, three high-accurate single-lead, instantaneous P- and T-wave detection algorithms were compared. These two applications could help in addressing cardiovascular diseases that are the number one cause of deaths worldwide. An effective and easy to handle arrhythmia classification algorithm could send people in risk early to a physician. High-accurate P- and T-wave detection algorithms requiring only a single lead are beneficial for all ECG monitoring fields, and at the same time, enhance the patient’s comfort. IMU-based activity recognition provides an objective method for classifying activities addressing the risk factor physical inactivity. Therefore, a common, publicly available dataset DaLiAc was created to enable the comparison of activity recognition algorithms (http://www.activitynet.org/). Using the benchmark dataset DaLiAc, a hierarchical classification system was created that outperformed six state of the art activity recognition algorithms. Recognizing single activities of daily living might increase the awareness of individuals to increase their physical activity as physical inactivity is one of the four leading risk factors for non-communicable diseases. In this thesis, three different wearable computing applications addressing different non-communicable diseases were presented. Wearables have been continuously used for health monitoring in recent years – with still increasing trend – as they bring certain benefits to the user in daily life. This will be enlarged in the future and fostered by ongoing inventions, accumulating knowledge, technological process, and progressive digitalization. 2025-12-16T05:34:08Z 2025-12-16T05:34:08Z 2025-12-15T15:04:33Z 2019 book ONIX_20251215T160010_9783961472611_46 https://library.oapen.org/handle/20.500.12657/109166 9783961472611 9783961472604 https://directory.doabooks.org/handle/20.500.12854/170242 eng FAU Studien aus der Informatik open access image/jpeg Attribution 4.0 International https://library.oapen.org/bitstream/20.500.12657/109166/1/9783961472611.pdf FAU University Press 10.25593/978-3-96147-261-1 10.25593/978-3-96147-261-1 2c600dea-eece-4066-87be-da335e323fdb 9783961472611 9783961472604 235 Erlangen open access |
| spellingShingle | Wearable Computing Smart Device Funktionsdiagnostik Wearable Computer Gesundheitstelematik thema EDItEUR::U Computing and Information Technology::UY Computer science::UYZ Human–computer interaction thema EDItEUR::U Computing and Information Technology::UK Computer hardware thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning Leutheuser, Heike Wearable computing applications in eHealth |
| title | Wearable computing applications in eHealth |
| title_full | Wearable computing applications in eHealth |
| title_fullStr | Wearable computing applications in eHealth |
| title_full_unstemmed | Wearable computing applications in eHealth |
| title_short | Wearable computing applications in eHealth |
| title_sort | wearable computing applications in ehealth |
| topic | Wearable Computing Smart Device Funktionsdiagnostik Wearable Computer Gesundheitstelematik thema EDItEUR::U Computing and Information Technology::UY Computer science::UYZ Human–computer interaction thema EDItEUR::U Computing and Information Technology::UK Computer hardware thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning |
| topic_facet | Wearable Computing Smart Device Funktionsdiagnostik Wearable Computer Gesundheitstelematik thema EDItEUR::U Computing and Information Technology::UY Computer science::UYZ Human–computer interaction thema EDItEUR::U Computing and Information Technology::UK Computer hardware thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning |
| url | ONIX_20251215T160010_9783961472611_46 |
| work_keys_str_mv | AT leutheuserheike wearablecomputingapplicationsinehealth |